AI Agent for Salesforce Batch Apex Generation

The Challenge

Writing batch Apex requires deep understanding of the Batchable interface, governor limits, state management, and error handling patterns. Poorly written batch jobs can fail silently, skip records, or lock the org during execution.

How Clientell Automates This

Clientell AI generates production-ready batch Apex classes complete with configurable scope sizes, comprehensive error logging, and optional job chaining. The generated code follows Salesforce best practices for stateful processing and includes retry logic for transient failures.

How It Works

1. 1

   Describe data operation

   Specify the object, filter criteria, field transformations, and any downstream actions that need to happen for each batch of records.

2. 2

   AI generates batch class

   Clientell AI produces the full Batchable class with start, execute, and finish methods, plus a companion scheduling class and error logging framework.

3. 3

   Review generated code

   Examine the batch logic, verify scope size recommendations, check error handling paths, and confirm that chaining sequences match your operational requirements.

4. 4

   Deploy to sandbox first

   Push the generated batch classes to a sandbox environment, run against a representative data set to validate behavior, then promote to production.

Frequently Asked Questions

How does the generated batch job handle partial failures?

Each execute method includes try-catch logic at the record level, logging failures to a custom object while continuing to process remaining records. A summary email is sent from the finish method detailing any errors.

Can Clientell generate chained batch jobs that run sequentially?

Yes. When you describe a multi-step data operation, Clientell generates separate batch classes that chain together using Database.executeBatch calls in the finish method of each preceding job.

Does the generated code include test classes?

Yes. Clientell generates companion test classes with bulk data setup, positive and negative test scenarios, and assertions that validate both successful processing and error handling paths.

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